University of Nebraska - LincolnDigitalCommons@University of Nebraska - LincolnSpecial Education and Communication DisordersFaculty Publications

Department of Special Education andCommunication Disorders

2014

The influence of audibility on speech recognitionwith nonlinear frequency compression for childrenand adults with hearing lossRyan W. McCreeryBoys Town National Research Hospital, ryan.mccreery@boystown.org

Joshua AlexanderPurdue University

Marc A. BrennanBoys Town National Research Hospital, marc.brennan@unl.edu

Brenda HooverBoys Town National Research Hospital

Judy KopunBoys Town National Research Hospital

See next page for additional authors

Follow this and additional works at: http://digitalcommons.unl.edu/specedfacpub

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McCreery, Ryan W.; Alexander, Joshua; Brennan, Marc A.; Hoover, Brenda; Kopun, Judy; and Stelmachowicz, Patricia G., "Theinfluence of audibility on speech recognition with nonlinear frequency compression for children and adults with hearing loss" (2014).Special Education and Communication Disorders Faculty Publications. 185.http://digitalcommons.unl.edu/specedfacpub/185

AuthorsRyan W. McCreery, Joshua Alexander, Marc A. Brennan, Brenda Hoover, Judy Kopun, and Patricia G.Stelmachowicz

This article is available at DigitalCommons@University of Nebraska - Lincoln: http://digitalcommons.unl.edu/specedfacpub/185

The influence of audibility on speech recognition with nonlinearfrequency compression for children and adults with hearing loss

Ryan W. McCreery, Ph.D.1, Joshua Alexander, Ph.D.2, Marc A. Brennan, Ph.D.1, BrendaHoover, M.A.1, Judy Kopun, M.A.1, and Patricia G. Stelmachowicz, Ph.D.1

1Hearing and Amplification Research Laboratory, Boys Town National Research Hospital,Omaha, NE

2Purdue University, West Lafayette, IN

Abstract

Objective—The primary goal of nonlinear frequency compression (NFC) and other frequency

lowering strategies is to increase the audibility of high-frequency sounds that are not otherwise

audible with conventional hearing-aid processing due to the degree of hearing loss, limited hearing

aid bandwidth or a combination of both factors. The aim of the current study was to compare

estimates of speech audibility processed by NFC to improvements in speech recognition for a

group of children and adults with high-frequency hearing loss.

Design—Monosyllabic word recognition was measured in noise for twenty-four adults and

twelve children with mild to severe sensorineural hearing loss. Stimuli were amplified based on

each listener’s audiogram with conventional processing (CP) with amplitude compression or with

NFC and presented under headphones using a software-based hearing aid simulator. A

modification of the speech intelligibility index (SII) was used to estimate audibility of information

in frequency-lowered bands. The mean improvement in SII was compared to the mean

improvement in speech recognition.

Results—All but two listeners experienced improvements in speech recognition with NFC

compared to CP, consistent with the small increase in audibility that was estimated using the

modification of the SII. Children and adults had similar improvements in speech recognition with

NFC.

Conclusion—Word recognition with NFC was higher than CP for children and adults with mild

to severe hearing loss. The average improvement in speech recognition with NFC (7%) was

consistent with the modified SII, which indicated that listeners experienced an increase in

audibility with NFC compared to CP. Further studies are necessary to determine if changes in

audibility with NFC are related to speech recognition with NFC for listeners with greater degrees

of hearing loss, with a greater variety of compression settings, and using auditory training.

Correspondence: Ryan McCreery, Boys Town National Research Hospital, Omaha, NE 68131, Fax: 402 452 5015, Phone: 402 4986668, Ryan.McCreery@boystown.org.

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Published in final edited form as:Ear Hear. 2014 ; 35(4): 440–447. doi:10.1097/AUD.0000000000000027.

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INTRODUCTION

Listeners who use conventional hearing-aid amplification often have reduced access to high-

frequency sounds due to the degree of hearing impairment, the limited bandwidth of the

hearing-aid receiver (<5000–6000 Hz) or a combination of both factors. Relative to wide-

bandwidth conditions (> 9000 Hz), limited bandwidth negatively impacts speech recognition

(Stelmachowicz et al. 2001) and novel word learning (Pittman 2009) in children with

hearing loss, as well as speech recognition (Ricketts et al. 2008) and sound quality ratings

(Moore & Tan 2003) in adults. Frequency-lowering signal processing strategies have been

implemented in hearing aids to improve the transmission of energy from the high-frequency

spectrum (see Simpson 2009, McCreery et al. 2012, and Alexander, 2013 for reviews). By

moving acoustic speech cues from high-frequency regions to lower frequencies where aided

audibility is better, these strategies can potentially increase the bandwidth that is accessible

to the listener.

This paper will focus on one specific form of frequency lowering, nonlinear frequency

compression (NFC), in which the input bandwidth above a specified start frequency is

compressed into a narrower bandwidth as determined by a specified compression ratio. After

processing with NFC, a wider range of frequencies is made audible to the listener, albeit

with reduced spectral detail. It is unknown how much the newly available information can

be used by individual listeners with hearing loss due to reductions in the distinctiveness of

acoustic speech cues caused by the bandwidth compression. As a first step, the current

investigation focused on documenting how much improvement in speech intelligibility with

NFC can be estimated using a model of audibility that attempts to quantify the amount of

speech information that is audible after lowering. The extent to which listeners

underperform compared to the model estimates will indicate how much intelligibility is

affected by the signal distortion introduced by the frequency compression. The results may

lead to the development of a more complex model of speech intelligibility for frequency-

lowering amplification in future studies.

The audibility of speech is often quantified using the Speech Intelligibility Index (SII; ANSI

S3.5-1997, R2007), which quantifies the proportion of the speech signal that is audible

based on the sensation level of speech in a number of discrete frequency bands. Because

frequency-lowering changes the distribution of frequency bands in the hearing aid output,

one approach to calculating audibility with NFC would be to estimate the sensation level for

each frequency band at the lower frequency where it occurs in the output, while retaining

individual band importance weights. Such an approach quantifies the amount of speech

information that is audible after lowering. This model does not account for how the usability

of speech information in audible bands of the input might be affected by frequency

lowering. Despite this potential limitation, McCreery et al. (2012) found that nonword

recognition for adults with normal hearing for stimuli processed with NFC followed a

predictable pattern based on the audible bandwidth. When more of the source bandwidth

was made audible by manipulating NFC start frequency and compression ratio, recognition

scores were higher. Whereas a predictable pattern based on audibility was observed for

listeners with normal hearing, results from listeners with hearing loss are needed to

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determine if a similar pattern would be observed in the population for which the processing

is intended.

Children and adults have had different patterns of speech recognition outcomes with NFC in

previous studies. Glista and colleagues (2009) conducted the only previous study that

included children and adults. Their results indicated that children were more likely than

older adults (50 – 81 years-old) to experience improvements with NFC compared to

conventional processing (CP). However, it is difficult to determine whether these findings

are due to developmental differences or to the fact that children had 5- to 10-dB more gain

than adults per the Desired Sensation Level prescriptive algorithm. Characterizing changes

in audibility could help to determine if age-related difference in speech recognition with

NFC are related to prescribing more audibility for children or developmental factors that

affect speech recognition. Children generally need a wider audible bandwidth to achieve

maximum levels of speech understanding (Stelmachowicz et al. 2001; McCreery &

Stelmachowicz, 2011), but the extent of this difference depends on the type of stimuli and

the task. Assuming that improvements in audible bandwidth are achieved with NFC, data

describing the developmental effects of bandwidth on speech recognition may be

informative for predicting outcomes.

The primary purpose of the present study was to determine if a modification of the SII that

estimated the difference in audibility between CP and NFC would reflect changes in speech

recognition in adults and children with mild to severe sensorineural hearing loss. The

primary hypothesis is that improvements in audibility with NFC will lead to improvements

in speech recognition. Because none of the listeners had experience listening with NFC

previously and previous research has suggested that speech recognition with NFC may

depend on experience (Wolfe et al. 2010; 2011; Glista et al. 2012), listeners were exposed to

NFC by listening to an audio-visual recording of stories processed with NFC. Speech

recognition was evaluated prior to and after the story. Based on previous research on the

influence of increasing bandwidth without frequency lowering on speech recognition, larger

improvements were expected for children than for adults.

METHOD

Participants

Twenty-four adults ages 19–65 years (mean =53.0, SD = 14.0) and 12 children ages 8–16

years old (mean = 12.0, SD=2.4) with mild to severe hearing loss participated in this study.

Figure 1 shows the mean (Left - X and Right - O) and range (shaded area) of thresholds for

listeners in the current study. At the time of enrollment in the study, ten of the adult listeners

had used binaural hearing aids for an average of 14 years (range: 0–29 years) with an

average reported daily use time of 11.2 hours (range 6–16). All of the children had used

binaural hearing aids for an average of 8 years (range: 4–15 years) with an average reported

daily use time of 9.6 hours (range: 5–15 hours) by parental report. None of the listeners in

either age group reported prior experience with any frequency lowering technology. While

using their personal amplification, the children’s articulation was screened using the

Bankson Bernthal Quick Screen of Phonology (BBQSP) and their vocabulary was assessed

using the Expressive Vocabulary Test, Form A (EVT-A). The articulation screening was

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used to determine the presence of significant articulation problems that could complicate

scoring, while the expressive language test was used to ensure that all of the children had

expressive vocabulary within the normal range for age. All children had BBQSP and EVT-A

results within the normal range for age. Listeners were paid $15/hour for their participation.

Children were also given a book of their choice.

Stimuli

Three hundred monosyllabic words containing one of 9 fricatives or affricates (/s/, /z/, /f/, /

v/, /ʧ/, /ʤ/, /ʃ/, /ʒ/, /θ/) in the initial or final position and six vowel contexts (/a/,/ i/, /I/, /ɛ/, /

u/, /ʌ/) were used to assess speech recognition with conventional processing and NFC.

Monosyllabic words were selected from word recognition tests (Phonetically Balanced

Kindergarten, Haskins, 1949; Word Intelligibility by Picture Identification, Ross & Lerman,

1971; California Consonant Test, Owens & Schubert, 1977; Computer Assisted Speech

Perception Assessment, Boothroyd, 2006) that were intended for children in the lower end

of the age range of listeners participating in this study. All of the words were determined to

be within the average child lexicon (Storkel & Hoover, 2010). Stimuli were spoken by a 22

year-old female talker. Recorded monosyllabic words were balanced for fricative/affricate

content across four 75-word lists, such that each list contained approximately the same

number of each fricative and affricate target in initial and final positions. Speech-shaped

competing noise was generated by computing a Fast Fourier Transform (FFT) of the female

talker, randomizing the phase of the signal at each frequency bin, and then taking the inverse

FFT. This process preserved the long-term average spectrum, but eliminated temporal and

spectral dips. The words were mixed with the speech-shaped noise at a signal to noise ratio

(SNR) of +6 dB. This SNR was chosen because speech recognition data for monosyllabic

words from McCreery et al. (2010) suggested that +6 dB SNR would not result in ceiling

performance.

Two stories were written to correspond to the pictures in the two children’s books “A Boy, a

Dog and a Frog” by Mercer Mayer and “One Frog Too Many” by Mercer and Marianna

Mayer. Vocabulary was selected that would maximize fricative content. Audio-visual

recordings were made of two preschool teachers of children with hearing loss reading the

stories. The head and shoulders of the teachers were filmed as they read the stories using a

JVC GY-HM710 HD Camera with a Fujinon Zoom Lens TH17X5BRMU. The video

resolution of the recording was 1920 × 1080 with a frame rate of 21 frames per second.

Video editing was completed using Final Cut Pro X software (Apple, Inc. Cupertino, CA).

Audio recordings of the stories were made using a Lectrosonics M175-LS wireless lavaliere

microphone connected to a Lectrosonics CR175 receiver at a sampling rate of 48 kHz and

16 bit resolution. The audio recordings were excised and then divided into 55–65 second

segments that corresponded to the pages in the books. Photographs taken of the book pages

were displayed for 5 seconds before and after each reading. The length of each story was 10

and 12 minutes for “A Boy, a Dog and a Frog” and “One Frog Too Many,” respectively.

Instrumentation

Stimulus presentation and audio response recording was performed using custom software

on a personal computer with a Lynx Studio Two-B sound card. Sennheiser HD-25-1 II

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headphones were used for stimulus presentation. A Shure 53 BETA head-worn boom

microphone with the standard filter cap was used to record listener responses for later

scoring. Videos of the stories were presented via a computer monitor using Max Runtime 5

software (Cycling ’74 Software, San Francisco, CA) to synchronize the processed audio and

video recordings.

Stimulus processing and hearing aid simulation

A hearing aid simulator was used to process stimuli in order to provide greater experimental

control over hearing-aid signal processing characteristics across listeners and conditions

than would be possible using real hearing aids. An 8-channel hearing aid simulator program

processed audio files for the monosyllabic words and stories. Two word lists were processed

using CP only, which included wide-dynamic range compression (WDRC) based on real-ear

aided response targets for Desired Sensation Level v5.0a (DSL; Scollie et al, 2005). Two

word lists were processed using NFC followed by WDRC. To determine the WDRC

settings, listener’s audiometric thresholds were converted to dB SPL using a transfer

function based on the TDH-50 headphones coupled with a KEMAR, which were then

entered into the DSL program. The DSL child algorithm was selected for the children, and

the DSL-adult algorithm without the binaural correction was selected for the adults. The

DSL program generated compression thresholds and ratios for each channel as well as real-

ear aided response target levels for 60 dB SPL speech and output limiting levels for each 1/3

octave frequency. Since DSL does not generate a target for 8000 Hz, target sensation levels

(SL) for 6000 Hz were used for 8000 Hz.

Presentation level for each listener was estimated with Sennheiser HD-25-1 II headphones

and an IEC 711 coupler attached to KEMAR using two iterations of the following steps.

First, the sound level of the long-term average speech spectrum based on a calibrated speech

signal from a male talker (the “carrot” passage) from the Verifit hearing-aid analyzer was

reproduced at 60 dB SPL to the input of the hearing aid simulator. Second, the output of the

hearing aid simulator was filtered into 1/3-octave bands (ANSI 2004), and the mean level of

each band in dB SPL was computed and compared to the prescribed DSL targets for a 60 dB

SPL speech input. Third, 1/3-octave band SPL levels were averaged according to which

WDRC channel they fell within and then the average difference between the output and

target level was calculated for each channel and used to adjust the gain in that channel. The

overall average difference between the output of the HA simulator and DSL targets for CP

across all octave frequencies and listeners was 0.686 dB (SD = 1.56). For individual

listeners, the average difference between HA simulator output and DSL targets across octave

frequencies from 250 Hz – 4000 Hz ranged from −0.99 dB to +1.83 dB. The largest

individual negative difference from prescriptive targets at an octave band frequency was

−4.83 dB for one listener at 2000 Hz, whereas the largest positive difference was +7.5 dB

for one listener at 250 Hz.

Amplification using amplitude compression (WDRC) and frequency-shaping of the stimuli

was accomplished for each listener using the MATLAB program reported in McCreery et al.

(2013). Specifically, attack and release times were set at 5 and 50 ms, respectively and

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referenced to the ANSI (2009) standard. Equation 8.1 from Kates (2008) was used to set the

gain control circuit.

if |x(n)| ≥ d(n−1)

d(n) = αd(n−1) + (1−α)|x(n)|

else

d(n) = βd(n−1)

end

where x(n) is the acoustic input signal, d(n) is the local mean level used to generate the gain

signal which was applied to x(n) to form the output signal, α is a constant derived from the

attack time, and β is a constant derived from the release time. When the signal is increasing

in level, the first part of the equation reduces gain, otherwise gain is increased as the signal

decreases in level. Minimum gain was limited to 0 dB and maximum gain to 65 dB.

Each pre-recorded digital sound file was filtered into 8 channels. The WDRC output above

the kneepoint recommended by DSL and below the output-limiting compression (OLC)

kneepoint was determined by the input signal level and the compression ratio. Above the

OLC kneepoint, the signal was compressed at a 10:1 ratio with 1 ms attack and 50 ms

release times. The maximum output was limited to 105 dB SPL and the signals were scaled

for output to the Sennheiser HD-25-1 II headphones. The maximum audible frequency for

CP conditions was limited at 5000 Hz for all listeners to simulate a plausible hearing aid

bandwidth based on the Phonak Naida SP hearing aid that was the basis for the simulation of

NFC. Gain for the last channel, which had a low-frequency crossover at 5000 Hz, was set to

0 dB and a 1024-tap low-pass filter was applied at 5000 Hz that reduced the output by 80 –

100 dB at 5500 Hz.

For the NFC conditions, signals were frequency compressed prior to WDRC using an

algorithm based on Simpson et al (2005). Overlapping 128-point Fast Fourier Transforms

(FFTs) were computed every 32 samples (1.45 ms) and used to estimate instantaneous

frequency. Instantaneous frequencies encompassing an input band of approximately 4500

Hz above the start frequency were synthesized at lower frequencies using phase vocoding;

with frequency-reassignment determined using Eq. (1) from Simpson et al. (2005). The 4500

Hz input band was selected to correspond to processing in the Phonak CORE signal

processing platform. The nominal start frequency was rounded to the nearest FFT bin, which

was a multiple of 172.26 Hz (22050 sampling rate/128 FFT rate). Following NFC, the

signals were amplified using the same amplitude compression parameters (gain,

compression thresholds and ratios, etc.) as for the CP condition.

NFC settings

Twelve combinations of start frequency and compression ratio were considered for each

listener. The combinations were selected from actual values found in the Phonak iPFG v2.0

fitting software assuming a Phonak Naida SP hearing aid and audiograms representing the

intended range of participants. Although the hearing-aid simulator allows for any potential

combination of start frequency and compression ratio, the available combinations for the

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group of listeners in the current study were selected to provide a more realistic

approximation of the combinations of start frequency and compression ratio that are

clinically available.

The NFC setting (combination of start frequency and compression ratio) was chosen for

each listener using a recommended clinical procedure (Alexander, 2013). The first step in

the recommended procedure is to document the highest frequency that can be made audible

with CP alone, known as the “maximum audible output frequency” since it determines the

output bandwidth available to the listener prior to remapping frequency with NFC. To

determine the maximum audible output frequency for each listener, audiometric thresholds

in dB SPL were plotted against 1/3-octave band spectral analysis of speech (“carrot”

passage) amplified with CP for a 65-dB input level. The maximum audible output frequency

was then estimated by graphically by locating the highest frequency where the long term

average speech spectrum (root-mean-square) intersected with thresholds. For each NFC

setting, the input frequency corresponding to the maximum audible output frequency was

computed using the equation from Simpson et al. (2005). The NFC setting that resulted in

the widest audible bandwidth was selected. The one exception was when multiple settings

produced reasonably close maximum audible output frequencies (operationally defined as

within 7%), then the setting with highest start frequency was selected in this group with the

intent of preserving formant spacing for vowels.

NFC settings were selected for both ears of each listener. In cases of audiometric

asymmetry, the NFC parameters were selected independently for each ear to maximize the

audible input bandwidth. All of the children had audibility up to 5000 Hz in both ears for CP

alone and so were assigned a start frequency of 3800 Hz and a compression ratio of 2.6:1

based on the procedures described above. Figure 2 plots the frequency input/output function

for CP and NFC for the children. The homogeneity of audiograms selected for the study and

controlled acoustic settings related to using headphones were likely the source of limited

variability in NFC parameters across listeners. For adults, the average frequency that was

audible with CP alone was 4630 Hz (range: 2500 – 5000 Hz), and the mean start frequency

was 3440 Hz (range: 1500 – 3800) with a mean compression ratio of 2.5:1 (range 2.2:1 –

3.2:1). With this range of start frequencies, input frequencies up to 8300 Hz were audible for

all listeners following NFC.

Audibility calculation

The audibility of the long-term average speech spectrum (LTASS) for the female talker for

this study was calculated for each listener and each ear using the SII (ANSI S3.5-1997,

R2007). The 1/3-octave-band method was used with a weighting function that assumed

equal importance across bands (1/18 = 0.0556), since specific importance weights were not

available for the stimuli used in the experiment. The calculation assumed a non-reverberant

environment. The band levels of the concatenated speech and noise stimuli were measured

using a Larson Davis System 824 sound level meter with a Larson Davis AEC 101 IEC 318

headphone coupler. The levels of speech and noise were converted to free-field using the

free-field to eardrum transfer function from the SII. Audiometric thresholds were converted

from dB HL to dB SPL and then interpolated and extrapolated to correspond with 1/3-

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octave-band frequencies. A frequency-specific bandwidth adjustment was used to convert

pure tone thresholds to equivalent 1/3-octave-band levels (Pavlovic, 1987). The spectrum

levels of speech and noise for each listener’s audiogram and condition (CP or NFC) were

entered into a program to calculate sensation level (SL) for each 1/3-octave band. For

conditions with CP, The SL for each band was multiplied by the importance weight for that

band, and the sum of these products for all bands generated the SII for each condition. For

conditions with NFC, the SII calculation was the same as for conditions with CP, except that

the SL for each frequency band above the start frequency was calculated at the frequency

where that band occurred in the output after NFC. Estimates of audibility for frequency-

compressed signals are noted as SII-NFC, whereas estimates of audibility from CP alone are

noted as SII-CP.

Procedures

All testing was completed in a sound-treated audiometric suite. Audiometric thresholds were

tested using TDH-49 earphones. Listeners were seated at a table in front of a computer

monitor. The listeners were instructed that they would hear real words and should repeat

each word. They were instructed to repeat the words exactly as they heard them. Listeners

viewed a slide show of landscapes and pictures on the computer to maintain their attention.

Listeners first completed a practice list of ten monosyllabic words with CP that were not part

of the lists used for experimental conditions. The words in the first half of the practice list

were in quiet and the second half were mixed with noise at +6 dB SNR. After the practice

list, stimuli were presented with one CP list of 75 words and one NFC list of 75 words,

referred to as the pre-exposure blocks. Following the pre-exposure blocks, all listeners were

exposed to the audio-visual presentation of the two stories. Listeners were instructed to

carefully attend because they would be asked 4 simple questions after each story. Responses

to the questions were recorded. All listeners correctly answered at least 7/8 questions, except

one child and one adult that each missed two questions. After the exposure, two more lists of

75 words, one with CP and one with NFC, were presented, referred to as the post-exposure

blocks. The processing types (CP or NFC) for pre- and post-exposure blocks were

counterbalanced across listeners using a modified Latin square design. The presentation

order of the stimuli within each word list was randomized. Whole words and phonemes were

scored as correct/incorrect after the experimental session by a single examiner using audio-

visual recordings of listeners’ responses. Ten percent of the trials were scored by a second

examiner who was blinded to the original scoring with a reliability of Cohen’s kappa =

0.9789 between the two examiners.

RESULTS

Figure 3 displays word recognition scores for each age group and condition. To address

questions related to the effect of processing (CP vs. NFC), exposure (pre- and post-exposure

interval), age (adult vs. child) and hearing aid use (yes vs. no) on speech recognition, a four-

way mixed analysis of variance was completed. There was a significant main effect of

processing [F(1,34)=27.6, p <0.001, ηp2 = 0.455] with NFC (M = 73.60% correct, SD =

11.82) greater than CP (M = 66.49% correct, SD =10.83). The main effect of exposure

interval was also significant [F(1,34)=13.0, p =0.001, ηp2 = 0.282] with higher post-

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exposure word recognition scores (M = 72.35%, SD=10.15) than pre-exposure scores (M =

68.66%, SD = 10.81). None of the other main effects or higher-order interactions reached

significance. Specifically, the lack of an age group by processing interaction [F(1,34)=1.3, p

=.268, ηp2 = 0.037], suggested that the main effect for processing (NFC > CP) was

consistent for both children and adults. The lack of a processing by exposure interval

interaction [F(1,34)=1.0, p=.328, ηp2 = 0.029], suggests that NFC exposure did not

preferentially benefit post-exposure word recognition in the NFC condition vs. the CP

condition. The lack of a main effect of age indicates that children and adults did not differ in

word recognition overall [F(1,34)=0.2, p=.630, ηp2 = 0.007] (M = 69.2% children; 70.8%

adults). Finally, the lack of a main effect of hearing aid use [F(1,34)=2.9, p=.101, ηp2 =

0.081] indicates that hearing aid users and non-users had similar speech recognition across

conditions.

Figure 4 displays individual differences between NFC and CP as a function of the difference

in audibility between conditions. The improvement in audibility between the SII-NFC and

SII-CP was calculated by subtracting the SII-CP from the SII-NFC to represent the increase

in potentially usable information with the specific NFC settings and audiogram for each

listener. The mean difference in audibility between SII-NFC and SII-CP was 0.1262 (SD

=0.031; range =0.09 – 0.22). Examination of the individual data suggested that most of the

participants had either improved speech recognition or no change in speech recognition with

NFC compared to CP.

DISCUSSION

The purpose of this study was to evaluate the influence of audibility on recognition of

speech processed by NFC in children and adults with mild to moderately-severe hearing

loss. In support of our hypothesis that increased audibility would support increased speech

recognition with NFC, word recognition with NFC was 7% higher than CP. In further

support of our hypotheses, this improvement in word recognition was consistent with an

index of audibility modified to account for the relocation of frequency bands following

NFC. Improvements in audibility were confined to frequencies above 3000 Hz in the current

study, which used NFC settings that were selected to maximize the bandwidth of the input

signal for listeners with mild to moderate high-frequency hearing loss. Contrary to our

hypotheses, children did not differ from adults in overall word recognition and did not

preferentially benefit from NFC.

The improvements in word recognition with NFC compared to CP are consistent with some

previous studies of children with hearing loss (Wolfe et al. 2010; 2011) and studies of both

children and adults with hearing loss (Glista et al. 2009). Adults with hearing loss have

shown less consistent improvements in speech recognition in investigations where the start

frequency was set at the likely maximum audible frequency so that audibility of the lowered

signal was limited (Simpson et al. 2005; 2006) or where frequency lowering parameters

were determined by each listener’s sound quality preference (Bohnert et al. 2010). Increased

audibility with NFC has been associated with improvements compared with CP in previous

research. In studies by Glista et al. (2009) and Wolfe et al. (2010; 2011), audibility of high-

frequency sounds was verified either behaviorally, electroacoustically or through a

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combination of both approaches. The current study verified audibility of high-frequency

sounds by estimating the audible bandwidth before and after the application of NLFC. While

a number of factors could have contributed to the variability in findings between studies and

within samples of listeners from the same studies, if NFC does not improve high-frequency

audibility or results in equivocal audibility, improvements in speech recognition would not

be anticipated.

In an effort to quantify changes in audibility with NFC in the current study, the SII was

adapted to account for the location of each 1/3-octave band in the output of the hearing aid

simulator using an importance function with equal weighting across frequency bands. The

improvement in audibility for listeners in this study was relatively small, since NFC

increased audibility for only the 6300 Hz and 8000 Hz bands for most listeners. However,

the amount of improvement in speech recognition with NFC compared to CP is similar to

previous studies that have compared speech recognition of children and adults with normal

hearing in conditions with and without the 8000 Hz octave band (McCreery &

Stelmachowicz, 2011). This suggests that when NFC is limited primarily to frequencies

above 3800 Hz, the magnitude of improvement in speech recognition with NFC is similar to

what has been observed in previous studies when high-frequency audibility is restored by

extending the bandwidth. For example, Hornsby et al. (2011) reported speech recognition of

adults with hearing loss under conditions with limited high-frequency audibility to wideband

conditions. Hornsby et al. reported either no difference or an improvement in speech

recognition for most of their adult listeners with hearing loss when the bandwidth of the

signal was extended above 4000 Hz, similar to the magnitude of the average effect observed

in the current study with NFC. Several subjects did not experience a significant change in

speech recognition with NFC. Two adults in the current investigation showed a significant

decrement in performance with NFC, despite an estimated improvement in audibility from

the SII-NFC. The two adult listeners who exhibited degradation in word recognition with

NFC did not differ from the group in terms of their NFC parameters, age, slope of hearing

loss or other factors that might have predicted reduced performance. Some listeners may

need greater listening experience with NFC to have improved speech recognition (Glista et

al. 2012) than was provided in this cross-sectional study. Clearly, the relationship between

audibility and speech recognition for frequency-lowered sounds is complex, and further

research is needed to resolve these issues.

Despite the positive results when the SII was adapted to account for frequency lowering, the

applicability of these estimates is currently limited and requires further validation.

Audibility is a necessary, but not sufficient condition for improving speech recognition in

listeners with hearing loss. The audibility-based index used in this study only reflects

information that can be transmitted after frequency lowering and does not account for

distortion or loss of spectral distinctiveness that may occur with compression. Many factors

likely determine how much information individual listeners can extract from the lowered

bands, including the frequency regions altered by lowering, the severity of loss in the

regions to which information is moved and the frequency resolution of the individual

listener with hearing loss. For example, NFC with a high start frequency that alters mostly

the broadband frication spectrum might improve fricative recognition, but the same amount

of frequency compression at a lower start frequency could alter vowel formant energy and

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have a negative effect on vowel recognition, although reduced vowel perception has not

been consistently documented in the literature. The SII-NFC as applied here assumes that

the contribution of each frequency band to speech recognition is the same regardless of

whether or not the spectrum has been altered by frequency lowering. Such an assumption

may be reasonable for conditions where NFC is limited to the high frequencies (>3800 Hz)

as in this study, but may not hold as the start frequency reaches frequency regions where the

frequency of vowel formant energy is important for perception. This model of speech

intelligibility, which assumes that speech cues made audible with NFC contribute to speech

recognition for the listener with hearing loss when spectrally distorted, may not yield

accurate predictions of speech recognition across a range of hearing losses or NFC settings.

Additionally, the monosyllabic words used for the speech recognition task in this study were

selected to contain phonemes with high-frequency energy, including fricatives and

affricates. Phonemes with high-frequency energy were targeted because those sounds may

be beyond the bandwidth of CP and potentially would be more audible with NFC. However,

the improvements observed with NFC for a stimulus set that has a higher occurrence of

fricatives and affricates than usually occur in language may overestimate the magnitude of

improvements and generalizability of the model of audibility to more realistic contexts. In a

study describing how speech perception with NFC changes over time, Glista and colleagues

(2012) reported differences across tasks (detection vs. recognition) and stimuli (phonemes

vs. nonwords). Likewise, Wolfe and colleagues (2010) found significant improvements for

phonemes and plural words, but not for sentences in noise. Clearly, the type of stimulus can

influence speech recognition with NFC. Further investigation into how task and stimulus

factors affect speech recognition with NFC will help to guide clinical decisions about how to

assess efficacy of frequency lowering.

The current investigation used a software-based hearing-aid simulator to allow for more

precise control over signal processing than would be possible using real hearing aids.

Although this decision provided greater experimental control over certain hearing-aid

parameters, differences between simulations of hearing-aid signal processing under

headphones and real hearing aids may impact the generalizability of these findings to

clinical populations. The use of headphones also prevented inclusion of listeners with

greater degrees of hearing loss because of the maximum output limitations of the

headphones. The simulator also capped the maximum audible frequency at 5 kHz with CP

for each listener, which may be more or less bandwidth than would be audible in real

hearing aids for listeners in this study depending on the degree of high-frequency hearing

loss. The decision to impose the same bandwidth for all participants for CP may have

resulted in more consistent differences in audibility between CP and NFC than would be

observed with wearable hearing aids. Further research should seek to replicate the use of

maximum audible frequency for selecting NLFC settings and SII-NFC using real hearing

aids and listeners with greater degrees of hearing loss.

Rather than drawing from an unlimited number of combinations of the start frequency and

compression ratio, the combinations of parameters used in the study were selected based on

the settings that were available in the manufacturer’s programming software. It may have

been possible to provide greater audibility with NFC by using different combinations of start

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frequencies and compression ratios than what were available with the manufacturer’s

programming software. However, such results would not be representative of NFC as

implemented in real hearing aids. Additionally, individually manipulating start frequency

and compression ratio yields multiple combinations of parameters that result in the same

audible bandwidth, and future research should directly evaluate the efficacy NFC settings by

independently manipulating start frequency and compression ratio. The improvements in

audibility provided by NFC may be less in this study than if start frequency and compression

ratio had been individually selected to provide the broadest bandwidth, but this study design

was more representative of what could be achieved with NFC as currently implemented in

hearing aids.

The adults and children with hearing loss in this investigation had mild to severe high-

frequency hearing loss that necessitated a relatively limited number of combinations of start

frequency and compression ratio compared to a clinical population. For that reason,

frequency lowering was constrained in most cases to frequencies above 3500 Hz. All of the

children and all but five of the adults had the same combination of start frequency (3800 Hz)

and compression ratio (2.6:1) due to similarities in the maximum audible frequency (5000

Hz) with CP through the hearing aid simulator. Therefore, the extent to which this method of

calculating audibility can be extended to listeners with greater degrees of hearing loss cannot

be determined from these results. Listeners with greater degrees of hearing loss may require

lower start frequencies and higher compression ratios to maximize audibility, which, as

noted, would lead to greater distortion of the speech spectrum. Increased spectral distortion

could make some high-frequency speech cues difficult to distinguish. For example, results

from Glista et al. (2009) suggest that some listeners may experience confusion of /s/ and /ʃ/

sounds with NFC, which could be anticipated with lower start frequencies that distort the

relationship and location of the spectral peaks for each of these sounds.

Conclusion

Adults and children with mild to severe high-frequency hearing loss demonstrated

improvements in speech recognition with NFC compared to CP. Improvements in audibility

were quantified using an adaptation of the SII based on the frequency compression settings

for each listener. The average improvement in audibility with NFC was equivalent to the

mean improvement in speech recognition. NFC improved audibility of the 6000 and 8000

Hz bands for most of the listeners. The amount of improvement in speech recognition with

NFC was similar to that observed in previous studies that extended the bandwidth for

frequency bands encompassing 6000 and 8000 Hz. Examination of individual data found

that several listeners had similar speech recognition with CP and NFC. Only two adult

listeners experienced degradation in speech recognition with NFC with improved audibility.

Future studies should seek to extend these approaches to listeners with greater degrees of

hearing loss to develop more complex models that could be used to support candidacy

decisions for frequency lowering.

Acknowledgments

The authors wish to express their thanks to Prasanna Aryal for computer programming assistance. This researchwas supported by grants to Dr. Stelmachowicz (R01 DC04300), Dr. McCreery (R03 DC012635), Dr. Brennan (F32

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DC012709), Dr. Alexander (RC1 DC010601) and the Post-doctoral training (T32 DC00013) and Human ResearchSubjects Core (P30-DC004662) grants to Boys Town National Research Hospital.

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Figure 1.The mean right ear (circles) and left ear (X) behavioral audiometric thresholds (dB HL) at

each test frequency (Hz) for adults (left panel) and children (right panel). The range of

thresholds for each age group is plotted as the hatched area.

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Figure 2.Example of output frequency (kHz) as a function of input frequency (kHz) for a start

frequency of 3800 Hz and a compression ratio (CR) of 2.6:1, which was the most common

combination of parameters for listeners in the study. Filled symbols represent frequencies

where the signal is audible. Open symbols represent frequencies where the signal is

inaudible. Squares represent conventional processing (CP), and circles represent nonlinear

frequency compression (NFC). The horizontal solid line represents the maximum audible

output frequency. The solid vertical line represents the maximum audible frequency (MAF;

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5000 Hz) with CP. The dashed vertical line represents the MAF (8094 Hz) with nonlinear

frequency compression (NFC).

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Figure 3.Percent correct word recognition across conditions of conventional processing and nonlinear

frequency compression before (PRE) and after (POST) the story processed with NFC. White

box plots are conventional processing (CP), and hatched box plots are nonlinear frequency

compression (NFC). Boxes represent the interquartile range (25th – 75th percentile) and

whiskers represent the 5th – 95th percentile range. The mean is plotted as a filled circle for

each condition, and the median is plotted as a solid horizontal line within each box.

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Figure 4.The difference in word recognition between conventional processing (CP) and nonlinear

frequency compression (NFC) as a function of the difference in estimated audibility between

the Speech Intelligibility Index for CP (SII-CP) and the SII for NFC (SII-NFC). Open circles

represent the difference for adults, and closed circles represent the difference for children.

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